Agriculture System for Potato Leaf Disease Detection Using Deep Learning And GENAI
Keywords:
Convolutional Neural Networks (CNNs), Deep Learning ,Disease detection, Generative Artificial Intelligence (GenAI), Image recognition, Potato leaf diseases, Smart Agriculture SystemAbstract
This study introduces a Smart Agriculture System designed to detect potato leaf diseases using advanced Deep Learning and Genetic Artificial Intelligence (GenAI) techniques. Potato, a vital global staple, is highly susceptible to various diseases that threaten yields and impose economic challenges on farmers. Traditional disease detection methods often fall short in efficiency, accuracy, and timeliness. To address these shortcomings, the proposed system employs Deep Learning algorithms, particularly Convolutional Neural Networks (CNNs), to analyze potato leaf images and identify disease signs accurately. CNNs excel in image recognition tasks by learning complex patterns and features associated with different leaf diseases, facilitating rapid and precise diagnoses. Additionally, GenAI enhances the system's performance by optimizing the Deep Learning model's hyperparameters and architecture, thus improving overall detection efficiency and effectiveness. By combining Deep Learning and GenAI, the Smart Agriculture System automates disease detection, ensuring early and accurate identification of potato leaf diseases. This proactive approach allows for timely interventions, such as targeted pesticide applications or crop management strategies, thereby mitigating disease spread and minimizing yield losses.
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